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Record W2550110194 · doi:10.1002/mds.26841

<i>C9orf72</i> and <i>ATXN2</i> repeat expansions coexist in a family with ataxia, dementia, and parkinsonism

2016· article· en· W2550110194 on OpenAlexaff
Ming Zhang, Zhengrui Xi, Karen Misquitta, Christine Sato, Danielle Moreno, Yan Liang, Elizabeth Slow, Ekaterina Rogaeva, Maria Carmela Tartaglia

Bibliographic record

VenueMovement Disorders · 2016
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversity Health NetworkToronto Western HospitalOccupational Cancer Research CentreUniversity of Toronto
Fundersnot available
KeywordsC9orf72Amyotrophic lateral sclerosisParkinsonismTrinucleotide repeat expansionFrontotemporal dementiaDementiaProbandGeneticsAlleleBiologyAtaxiaMedicineDiseaseMutationPathologyNeuroscienceGene

Abstract

fetched live from OpenAlex

BACKGROUND: Intermediate interrupted ataxin 2 (ATXN2) alleles (27-33 CAG-repeats) increase the risk for amyotrophic lateral sclerosis and are reported as modifiers in chromosome 9 open reading frame 72 (C9orf72) carriers, rendering susceptibility to amyotrophic lateral sclerosis rather than frontotemporal lobar degeneration. The clinical presentation of C9orf72 patients with pathogenic ATXN2 alleles (≥35 CAG-repeats) is unknown. METHODS: Blood samples were collected from a family affected by ataxia, dementia, and parkinsonism, but not amyotrophic lateral sclerosis. Mutation analyses of the proband included C9orf72 and 14 ataxia genes, followed by segregation analyses in family members. RESULTS: -repeat allele in C9orf72 that is typical of large pathogenic expansions. CONCLUSIONS: The CAG-expansion in ATXN2 likely caused the ataxia, whereas the dementia may be linked to both C9orf72 and ATXN2 repeat expansions. The pathological uninterrupted ATXN2 repeat may not have the same modifying effect as intermediate interrupted alleles. © 2016 International Parkinson and Movement Disorder Society.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.245
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2016
Admission routes1
Has abstractyes

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